Peer risk benchmarking using generative adversarial networks

ABSTRACT

A method, computer system, and computer program product are provided for peer risk benchmarking. Customer data for a first network is obtained, wherein the customer data comprises a role of one or more network devices in the first network and a plurality of risk reports corresponding to the one or more network devices, and wherein each risk report is associated with a particular dimension of a plurality of dimensions of risk for the one or more network devices. A network profile image is generated by processing the plurality of risk reports. A generative adversarial network generates a synthetic network profile image from the network profile image, wherein the synthetic network profile image does not include the customer data. A second network is evaluated using the synthetic network profile image to identify differences between the first network and the second network.

TECHNICAL FIELD

The present disclosure relates to cybersecurity, and more specifically,to peer risk benchmarking using generalized adversarial networks.

BACKGROUND

In the field of cybersecurity, peer benchmarking refers to the comparingof an enterprise to other similar enterprises in order to makedecisions, such as whether to upgrade, modify, or expand infrastructurecomponents. Since much of a network's infrastructure can go unnoticed byend users, it can be difficult to justify making purchases to change thenetwork in the absence of glaring performance issues. Thus, potentialissues may often be identified by benchmarking a network against thenetworks of peers in order to bring awareness to the shortcomings of thebenchmarked network. However, due to privacy concerns and otherlimitations, organizations are frequently unable to identify and obtainthe data necessary to perform peer benchmarking.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram depicting an environment for peer riskbenchmarking, in accordance with an example embodiment.

FIG. 2 is a block diagram depicting generation of peer risk benchmarkingdata, in accordance with an example embodiment.

FIG. 3 is a histogram depicting a distribution of a dimension of risk,in accordance with an example embodiment.

FIG. 4 is a radar chart depicting a multi-dimensional assessment ofrisk, in accordance with an example embodiment.

FIGS. 5A and 5B are diagrams depicting network profile images, inaccordance with an example embodiment.

FIG. 6 is a block diagram depicting a generative adversarial network inaccordance with an example embodiment.

FIG. 7 is a block diagram depicting an evaluation of risk, in accordancewith an example embodiment.

FIG. 8 is a flow chart depicting a method for generating synthetic peerrisk benchmarking data, in accordance with an example embodiment.

FIG. 9 is a flow chart depicting a method for performing peer riskbenchmarking, in accordance with an example embodiment.

FIG. 10 is a block diagram depicting a computing device configured tomodify a web application at runtime, in accordance with an exampleembodiment.

DESCRIPTION OF EXAMPLE EMBODIMENTS Overview

According to one embodiment, techniques are provided for peer riskbenchmarking. Customer data for a first network is obtained, wherein thecustomer data comprises a role of one or more network devices in thefirst network and a plurality of risk reports corresponding to the oneor more network devices, and wherein each risk report is associated witha particular dimension of a plurality of dimensions of risk for the oneor more network devices. A network profile image is generated byprocessing the plurality of risk reports. A generative adversarialnetwork generates a synthetic network profile image from the networkprofile image, wherein the synthetic network profile image does notinclude the customer data. A second network is evaluated using thesynthetic network profile image to identify differences between thefirst network and the second network.

Example Embodiments

Embodiments are provided for cybersecurity, and more specifically, forpeer risk benchmarking using generalized adversarial networks.

Peer benchmarking refers to comparing aspects of an enterprise,including networking and computing components, to other similarenterprises in order to make informed decisions. In the field of riskanalysis, peer benchmarking may involve determining whether theparticular network configuration of an organization exposes theorganization to more or less risk as compared to other similarorganizations (i.e., “peers”). For example, a particular version of anoperating system used in network devices may be more susceptible tovulnerabilities compared to the operating system used by peers. When anorganization identifies that its operating system version places theorganization at greater risk, the organization may accordingly decide toupgrade network devices to a newer version of the operating system.Thus, peer risk benchmarking enables an organization to be aware whentheir infrastructure differs from the infrastructure of peers in termsof exposure to risk.

In order to perform peer risk benchmarking of a network, an organizationanalyzes data relating to the networks of peers. However, obtaining thisdata can be difficult due to data privacy issues. For example, anorganization may not be able to share some data due to statutoryrequirements or because the data contains trade secrets. Moreover, someorganizations may have unique infrastructure configurations, and thushave few comparable peers. Accordingly, present embodiments enable thegeneration of peer benchmarking data that simulates the data of anorganization's network, but does not actually contain any of theorganization's data. A machine learning model is trained to generatesynthetic network profiles that accurately reflect an organization'srisk across multiple dimensions. This simulated data can thus safely beshared with other organizations for benchmarking or other purposes.Accordingly, present embodiments enable the generation of peerbenchmarking data that completely avoids exposure of private orsensitive data, and that enables the risk of networks to be assessedacross a wide range of dimensions of risk.

It should be noted that references throughout this specification tofeatures, advantages, or similar language herein do not imply that allof the features and advantages that may be realized with the embodimentsdisclosed herein should be, or are in, any single embodiment. Rather,language referring to the features and advantages is understood to meanthat a specific feature, advantage, or characteristic described inconnection with an embodiment is included in at least one embodiment.Thus, discussion of the features, advantages, and similar language,throughout this specification may, but do not necessarily, refer to thesame embodiment.

Furthermore, the described features, advantages, and characteristics maybe combined in any suitable manner in one or more embodiments. Oneskilled in the relevant art will recognize that the embodiments may bepracticed without one or more of the specific features or advantages ofa particular embodiment. In other instances, additional features andadvantages may be recognized in certain embodiments that may not bepresent in all embodiments.

These features and advantages will become more fully apparent from thefollowing drawings, description and appended claims, or may be learnedby the practice of embodiments as set forth hereinafter.

Embodiments are now described in detail with reference to the figures.FIG. 1 is a block diagram depicting an environment 100 for peer riskbenchmarking in accordance with an example embodiment. As depicted,environment 100 includes customer networks 102A-102N, one or more riskreport servers 112A-112N, a risk benchmarking server 120, and a network136. It is to be understood that the functional division amongcomponents of environment 100 have been chosen for purposes ofexplaining various embodiments and is not to be construed as a limitingexample.

Customer networks 102A-102N each include one or more network devices104A-104N and network data 110; each network device 104A-104N mayinclude a network interface (I/F) 106 and at least one processor 108. Invarious embodiments, each network device 104A-104N may include a server,a router, a hub, a switch, a bridge, a gateway, a modem, a repeater, anaccess point, a firewall, an endpoint device (e.g., a laptop computer, atablet computer, a netbook computer, a personal computer (PC), a desktopcomputer, a personal digital assistant (PDA), or a smart phone) or anyother programmable electronic device capable of executing computerreadable program instructions and performing networking-relatedoperations in the respective customer networks 102A-102N. Moreover, thenetwork devices 104A-104N may be virtual in the sense that they areembodied as software running on a computing device. Network interface106 enables each network device 104A-104N to send and receive data overa network, such as network 138 and/or customer networks 102A-102N. Ingeneral, network devices 104A-104N may perform any networking orcomputing task, including transmitting, receiving, and/or processing ofdata obtained from, or provided to, network-accessible computingdevices, including devices internal and/or external to customer networks102A-102N. Each network device 104A-104N may include internal andexternal hardware components, as depicted and described in furtherdetail with respect to FIG. 10.

Network data 110 for each customer network 102A-102N may include anydata relating to dimensions of risk that are being assessed for customernetworks 102A-102N in accordance with present embodiments. As such,network data 110 may include an inventory of network devices 104A-104Nfor a particular customer network 102A-102N, including hardwarespecifications, a list of installed software and firmware, roles of eachnetwork device 104A-104N in the particular customer network 102A-102N,and any other data or metadata. Thus, network data 110 can indicatedevice roles and/or the topology of any of customer networks 102A-102N.

Additionally or alternatively, network data 110 may include risk reportsfor one or more dimensions of risk. The risk reports can be obtainedfrom one or more sources that are internal to and/or external tocustomer networks 102A-102N. For example, risk reports can be generatedby network devices 104A-104N, and/or can be obtained from risk reportservers 112A-112N.

The risk reports that are included in network data 110 may becategorized according to one or more dimensions of risk. Dimensions ofrisk can include a security advisories dimension, a field noticesdimension, a bug reports dimension, a service requests dimension, asystem logs dimension, an end-of-life dimension, a licensing dimension,and best practices dimension. Risk reports in a security advisoriesdimension may include security advisory reports, such as new viruses,worms, or other malware, zero-day vulnerabilities, currentdenial-of-service attacks, and the like. Risk reports in a field noticesdimension may include upgrades, workarounds, or other changes tohardware and/or software of network devices 104A-104N. Risk reports in abug reports dimension may include descriptions of bugs submittedautomatically by network devices 104A-104N and/or by users of networkdevices 104A-104N. Bug reports can also be issued by third parties, suchas vendors or developers, and can be obtained from servers associatedwith those third parties (e.g., risk report servers 112A-112N). Riskreports in a service requests dimension may include requests from usersfor some service to be provided by a network administrator, such aserror troubleshooting to be provided, software to be installed, hardwareto be upgraded, and the like. Risk reports in a systems log dimensioncan include any logs generated by network devices 104A-104N, eitherautomatically or at the request of a user. Risk reports in anend-of-life dimension may indicate a lifecycle of the hardware and/orsoftware, including a planned end date for customer support, updates,hotfixes, availability of replacement parts, and the like. Risk reportsin a licensing dimension may include any information relating tolicensed software, including terms of license agreements, duration oflicense agreements, and the like. Risk reports in a best practicesdimension may include descriptions of recommended settings orconfigurations of the hardware and/or software components of networkdevices 104A-104N, such as recommendations to use a particular dataencryption mechanism, a recommended firewall rule, a suggested passwordstrength, and the like. Each risk report can also include an indicationof a severity of risk, such as a “low” risk, “medium” risk, or “high”risk, or the risk severity can be indicated according to a numericalscale or other metric.

Risk report servers 112A-112N each include a network interface (I/F)114, at least one processor 116, and a database 118. Each risk reportserver 112A-112N may include a rack-mounted server, or any otherprogrammable electronic device capable of executing computer readableprogram instructions. Network interface 114 enables components of eachrisk report server 112A-112N to send and receive data over a network,such as network 136. Risk report servers 112A-112N may include internaland external hardware components, as depicted and described in furtherdetail with respect to FIG. 10.

In general, each risk report server 112A-112N provides, or otherwisemakes available, risk reports for networks of computing devices, such ascustomer networks 102A-102N and network devices 104A-104N. Each riskreport server 112A-112N may be associated with one or more providers ofhardware and/or software that is in use by any of network devices104A-104N. Each risk report may indicate impacted hardware and/orsoftware modules, and each risk report may include a description of therisk and/or the nature of the impact.

Database 118 may include any non-volatile storage media known in theart. For example, database 118 can be implemented with a tape library,optical library, one or more independent hard disk drives, or multiplehard disk drives in a redundant array of independent disks (RAID).Similarly, data in database 118 may conform to any suitable storagearchitecture known in the art, such as a file, a relational database, anobject-oriented database, and/or one or more tables. Database 118 maystore data relating to risk reports, including new and previous riskreports. Database 118 may make risk reports accessible, via network 136,to external entities, such as network devices 104A-104N of customernetworks 102A-102N and risk benchmarking server 120. Additionally oralternatively, risk report servers 112A-112N may provide data stored indatabase 118 to external destinations, either on an ad hoc basis (e.g.,as reports become available) or according to a predetermined schedule.

Risk benchmarking server 120 includes a network interface (I/F) 122, atleast one processor 124, memory 126, and database 134. Memory 126 storessoftware instructions for a network analysis module 128, a datageneration module 130, and a benchmarking module 132. Risk benchmarkingserver 120 may include a rack-mounted server, or any other programmableelectronic device capable of executing computer readable programinstructions. Network interface 122 enables components of riskbenchmarking server 120 to send and receive data over a network, such asnetwork 136. In general, risk benchmarking server 120 enables theassessment of risk for a computing network, such as any of customernetworks 102A-102N. Risk benchmarking server 120 may include internaland external hardware components, as depicted and described in furtherdetail with respect to FIG. 10.

Network analysis module 128, data generation module 130, andbenchmarking module 132 may include one or more modules or units toperform various functions of the embodiments described below. Networkanalysis module 128, data generation module 130, and benchmarking module132 may be implemented by any combination of any quantity of softwareand/or hardware modules or units, and may reside within memory 126 ofrisk benchmarking server 120 for execution by a processor, such asprocessor 124.

Network analysis module 128 may obtain and analyze customer datarelating to a network (e.g., network data 110) in order to create amultidimensional assessment or representation of risk for the network.The values for each dimension of risk in a multidimensional assessmentof risk can be normalized such that a multidimensional assessment ofrisk for one customer network can be compared to a multidimensionalassessment of risk for another customer network. For example, for eachdimension of risk, network analysis module 128 may determine a riskscore that is normalized within a range of values, such as zero to fiveor zero to ten, with higher values indicating greater risk exposure.

In particular, network analysis module 128 may analyze network data 110of any customer network 102A-102N to assess risk in any number ofdimensions, including a security advisories dimension, a field noticesdimension, a bug reports dimension, a service request dimension, asystem logs dimension, a network device roles dimension, a networktopology dimension, an end-of-life dimension, a licensing dimension, anda best practices dimension. In order to determine a risk score for adimension of risk, network analysis module 128 may analyze all of therisk reports for that dimension, which are included in network data 110.In some embodiments, each risk report is associated with a particularvalue based on the content of the risk report, date and/or time of therisk report, and/or metadata associated with the risk report. In someembodiments, each risk report is assigned a particular value by areviewer. These values can be processed using statistical ormathematical operations to obtain an overall risk score for thedimension of risk. For example, the values assigned to risk reports maybe summed to obtain an overall risk score, or the risk score may beequal to the mean, median or mode value. The overall risk score for eachdimension of risk may then be normalized and included in themultidimensional assessment of risk for the network.

In some embodiments, the security advisories, field notices, bestpractices, bug reports, bug reports, service requests, end-of-life,licensing dimensions and/or systems logs dimensions are scored based onpredetermined values associated with the software and/or hardwareelements impacted or otherwise described in the risk reports for eachdimension. For example, a security advisory relating to a firewallsecurity threat may have a higher predetermined score, indicating asgreater exposure to risk, than a security advisory relating to aperipheral device such as a printer.

In some embodiments, the security advisories, field notices, bestpractices, bug reports, bug reports, service requests, end-of-life,licensing dimensions and/or systems logs dimensions are scored based onthe time of each risk report. For example, older risk reports thatindicate exposures to risk that have not yet been remediated may beassociated with high risk scores. Similarly, risk can be scored in alicensing or end-of-life dimension based on the imminence of expirationof each licensed product or end of support for each product. Forexample, licenses whose expiration dates are upcoming (e.g., within amonth) may be associated with more risk than licenses that will notexpire for several months or years.

In some embodiments, the network device roles dimension is scored basedon the roles of each network device 104A-104N in a network. For example,core devices can be assigned higher values than edge components; thevalues for all devices can then be processed to obtain an overall riskscore. In some embodiments, the network topology dimension is scoredbased on the configuration of the network. For example, a networktopology score can be predetermined based on whether the networkconforms to a mesh topology, star topology, bus topology, ring topology,or hybrid topology.

In some embodiments, network analysis module 128 generates a networkprofile image representing the multidimensional assessment of risk for anetwork. For example, a radar chart can be constructed with a number ofaxes corresponding to the number of dimensions of risk that areassessed. Images generated by network analysis module 128 are depictedand described in further detail with respect to FIG. 4.

Data generation module 130 may generate synthetic data, based on thenetwork profile images generated by network analysis module 128, thatcan be used for peer risk benchmarking. In particular, data generationmodule 130 may utilize a trained machine learning model to generatesynthetic network profile images that are similar to the network profileimages of actual networks (e.g., customer networks 102A-102N), but donot contain any actual customer data.

In some embodiments, data generation module 130 utilizes a conventionalor other generative adversarial network to generate synthetic networkprofile images based on actual network profile images. A generativeadversarial network includes a generator network that generatessynthetic images, and a discriminator network that compares thegenerated synthetic images to the actual images to distinguish imagesproduced by the generator from the data distribution of the actualimages. In some embodiments, the generative adversarial network is adeep convolutional generative adversarial network in which the generatornetwork and discriminator network are deep convolutional neuralnetworks. Using the generative adversarial network, data generationmodule 130 outputs synthetic network profile images that are stored indatabase 134 for use in peer risk benchmarking. The generativeadversarial network that may be used by data generation module 130 isdepicted and described in further detail with respect to FIG. 6.

Benchmarking module 132 performs peer risk benchmarking by comparingnetwork profile images of actual networks (e.g., customer networks102A-102N) to synthetic profile images generated by data generationmodule 130. Since the synthetic profile images closely approximate therisk profile of actual networks but do not contain any data from thosenetworks, benchmarking module 132 can accurately evaluate a customernetwork to determine how the network would compare to networks of peersin terms of risk. In order to evaluate a network, benchmarking module132 may obtain a network profile image corresponding to the network fromdata generation module 130.

In particular, benchmarking module 132 may extract the risk score ofeach dimension of risk from a synthetic network profile image, andcompare those risk scores to corresponding risk scores of an evaluatednetwork. Benchmarking module 132 may evaluate a network against severalsynthetic datasets that are generated by data generation module 130 inorder to determine how the evaluated network compares to its peers. Forexample, benchmarking module 132 may determine that an evaluated networkranks at a 95^(th) percentile as compared to its peers in terms of onedimension of risk, but only ranks at a 50^(th) percentile in anotherdimension of risk. Thus, benchmarking module 132 can identify particulardimensions of risk of an evaluated network that can be targeted forimprovement.

Database 134 may include any non-volatile storage media known in theart. For example, database 134 can be implemented with a tape library,optical library, one or more independent hard disk drives, or multiplehard disk drives in a RAID. Similarly, data in database 134 may conformto any suitable storage architecture known in the art, such as a file, arelational database, an object-oriented database, and/or one or moretables. Database 134 may store data relating to peer risk benchmarking,including network profile images relating to customer networks102A-102N, synthetic network profile images generated by data generationmodule 130, and data corresponding to actual and synthetic networkprofile images, such as risk scores for each dimension of risk. In someembodiments, database 134 only retains customer data (e.g., network data110 obtained from customer networks 102A-102N and/or network profileimages based upon network data 110) temporarily in order to evaluatecustomer networks and/or generate synthetic network profile images basedon the customer data. In order to ensure data privacy, customer data maybe deleted from database 134 once the data has been used for evaluationpurposes and/or to generate synthetic network profile images.

Network 136 may include a local area network (LAN), a wide area network(WAN) such as the Internet, or a combination of the two, and includeswired, wireless, or fiber optic connections. In general, network 136 canbe any combination of connections and protocols known in the art thatwill support communications between network devices 104A-104N ofcustomer networks 102A-102N, risk report servers 112A-112N, and/or riskbenchmarking server 120 via their respective network interfaces inaccordance with the described embodiments.

Referring to FIG. 2, FIG. 2 is a block diagram 200 depicting generationof peer risk benchmarking data, in accordance with an exampleembodiment.

Initially, network data 110 from a customer network, such as any ofcustomer networks 102A-102N, is obtained and provided to networkanalysis module 128. The network data 110 may include any risk reports,such as security advisories, field notices, bug reports, servicerequests, system logs, end-of-life reports, licensing reports, and bestpractices reports, as well as data relating to network device roles, andnetwork topology. Network analysis module 128 may process the riskreports and network device roles and topology data to determine a riskscore for each dimension of risk, which are normalized to a same rangeof values.

Using the normalized risk scores for each dimension of risk, networkanalysis module 128 can generate a risk assessment 210, which mayinclude a radar chart having axes corresponding to the dimensions ofrisk. The risk scores for each dimension of risk can form vertices of apolygon, which can be processed to produce network profile image 220.The polygon extracted from risk assessment 210 can be transformed usingconventional or other transform operations and/or upscaled usingconventional or other upscaling techniques in order to generate networkprofile image 220. In some embodiments, the transform operations and/orupscaling applied to create network profile image 220 exaggeratesfeatures of the risk profile in order to more easily highlightdifferences when network profile image 220 is compared to syntheticnetwork images during the benchmarking process.

Benchmarking module 132 may compare network profile image 220 to one ormore synthetic network images, generated based on peer network data, inorder to perform a multi-dimensional evaluation of the network profileimage 220. For example, benchmarking module 132 may compare the shape ofnetwork profile image 220 to the shape of synthetic network images,identify areas where the shapes differ, and determine which dimensionsof risk are associated with those areas. Moreover, benchmarking module132 can quantify the difference by converting the shapes back to riskscores for each dimension of risk, thereby computing the numericaldifferences for each dimension of risk. A benchmarking report 230 may beoutput that summarizes differences in risk between the evaluated networkand the synthesized data based on one or more peer networks.

Referring to FIG. 3, FIG. 3 is a histogram 300 depicting a distributionof a dimension of risk, in accordance with an example embodiment. Asdepicted, histogram 300 indicates a distribution of risk reports in abest practices medium dimension of risk. Each risk report may beprovided a particular value, and risk reports sharing a same assignedvalue can be grouped. The number of risk reports in a group can then becounted, and the histogram can be constructed using normalized values.In the depicted example, risk reports having an assigned value of around30 are the most common, as indicated by the normalized count of 0.16. Anoverall score for the best practices medium dimension of risk can bedetermined by selecting the highest value (e.g., the value associatedwith the maximum on histogram 300) and normalizing that value. Inparticular, the value may be normalized by subtracting the mean of allgroup counts from the largest group count, and then dividing theresulting value by the standard deviation of the group count, resultingin an overall risk score.

Referring to FIG. 4, FIG. 4 is a radar chart 400 depicting amultidimensional assessment of risk, in accordance with an exampleembodiment. As depicted, radar chart 400 assesses risk in a securityadvisories dimension, a field notices dimension, a bug reportsdimension, a service request dimension, a system logs dimension, anetwork device roles dimension, a network topology dimension, anend-of-life dimension, a licensing dimension, and a best practicesdimension. The individual risk scores for each dimension can beindicated on radar chart 400, with values associated with increasedexposure to risk being farther from the origin. Thus, radar chart 400represents operational and/or performance risk for a network, such asany of customer networks 102A-102N. An image representing the riskprofile of the network can be extracted from radar chart 400 bygenerating a polygon having vertices defined by the risk scores alongeach axis, where the relative position of the risk scores indicate theshape of the polygon.

Referring to FIG. 5A, FIG. 5A is a diagram depicting a network profileimage 500, in accordance with an example embodiment. Network profileimage 500 may thus be a risk profile distribution that represents riskof an actual customer network based on network data (e.g., network data110). Network profile image 500 may be generated based on a polygonextracted from a radar chart, such as radar chart 400 of FIG. 4, inwhich risk values for each dimension of risk are plotted oncorresponding axes. Next, one or more transform operations and/orupscaling may be applied to the polygon to generate network profileimage 500.

Referring to FIG. 5B, FIG. 5B is a diagram depicting a synthetic networkprofile image 550, in accordance with an example embodiment. Syntheticnetwork profile image 550 may be a generated customer profiledistribution produced by inputting the network profile image 500 into agenerative adversarial network. Thus, synthetic network profile image550 is similar to network profile image 500 in terms of the values ofdimensions of risk represented in each image, but synthetic networkprofile image 550 does not include any of the underlying data (e.g.,network data 110) used to generate network profile image 500.

Referring to FIG. 6, FIG. 6 is a block diagram depicting a generativeadversarial network 600 in accordance with an example embodiment.Generative adversarial network 600 includes a generator network 610 anda discriminator network 620. In some embodiments, generator network 610and discriminator network 620 are deep convolutional neural networks.Generative adversarial network 600 may be trained using network profileimages based a sample network. In particular, the risk scores for eachdimension of risk of a network are converted to 32-by-32 image arraysfor each of three classes of network devices, such as a data centerswitch class, a local area network switch class, and a router class.

Generator network 610 may process customer profile images to generatesimilar images that do not include customer data (e.g., generatedprofile images 630). In particular, generator network 610 may mapvectors of shapes, obtained by constructing radar charts usingmultidimensional risk scores, to the 32-by-32 image arrays for eachcategory of network device. Discriminator network 620 may then map theimage arrays to a binary score that estimates the probability that theimage is real (e.g., generated based on actual customer network data) ornot. Generative adversarial network 600 chains generator network 610 anddiscriminator network 620 together so that latent space vectors aremapped to the estimated probabilities of images being real as assessedby discriminator network 620. Discriminator network 620 is trained usinglabeled data, such as images that are labeled as based on actualcustomer networks (e.g., real customer profile images 640), and imagesthat are labeled as not based on actual customer networks (e.g.,generated profile images 630 and/or other images). During training, theweights of elements in generator network 610 are iteratively adjustedusing a loss function in order to output images that are perceived bydiscriminator network 620 as real, despite being generated by generatornetwork 610. When generator network 610 is able to generate images thatare incorrectly assessed as real by discriminator network 620 beyond athreshold rate (e.g., 9 times out of 10 or 90%), training is complete.

Referring to FIG. 7, FIG. 7 is a block diagram 700 depicting anevaluation of risk, in accordance with an example embodiment. A networkprofile image 710, based on actual network data of a customer networkbeing evaluated, can be provided to a computing device or module, suchas benchmarking module 132 of risk benchmarking server 120. One or moresynthetic network profile images 720 are also provided, and networkprofile image 710 is compared to each provided synthetic network profileimage 720. In some embodiments, benchmarking is performed on a cloudplatform. Benchmarking data is generated that highlights any differencesin any dimension of risk between the evaluated network and the syntheticpeer risk benchmarking data represented by the one or more syntheticnetwork profile images 720. For example, the benchmarking data mayindicate that the evaluated network is ranked at a certain percentile ofrisk, for each dimension of risk assessed, as compared to peers.

Referring to FIG. 8, FIG. 8 is a flow chart depicting a method 800 forgenerating synthetic peer risk benchmarking data, in accordance with anexample embodiment.

Customer data for a network, including the roles of network devices andrisk reports, is obtained at operation 810. The customer data mayinclude a description of roles of network devices and the topology ofthe network, as well as risk reports for each dimension of risk that isbeing assessed. For example, risk reports may include best practices,security advisories, field notices, bug reports, service requests,system logs, end-of-life reports, licensing reports, and the like.Additionally, risk reports can be divided into further dimensions basedon the severity of reports, such as “low” or “high”, for each riskreport dimension.

A network profile image is generated based on the customer data atoperation 820. Initially, a risk score may be determined for eachdimension of risk based on the corresponding risk reports, descriptionsof roles of network devices, and/or topology of the network. Theresulting individual risk scores can together be converted into anetwork profile image that depicts risk across all assessed dimensions.For example, the risk scores may be used to construct a radar chart,such as radar chart 400 shown in FIG. 4, from which a polygon-shapednetwork profile image can be extracted. Transform operations and/orupscaling operations may be applied to finalize the network profileimage, and may exaggerate features that represent risk in order to moreeasily identify differences between compared images.

A generative adversarial network is applied to generate a syntheticnetwork profile image based on the customer data at operation 830. Thegenerative adversarial network may be trained to generate syntheticnetwork profile images that are similar to real network profile images,but do not contain any actual network data from real networks (e.g.,customer networks 102A-102N).

The synthetic network profile image is saved at operation 840. Syntheticnetwork profile images may be stored for later use in peer benchmarkingtasks. For example, the synthetic network profile image may be stored indatabase 134 of risk benchmarking server 120. In some embodiments, whena synthetic network profile image is generated and stored, theunderlying network data and the network profile image used to generatethe synthetic network profile image is securely deleted to ensure dataprivacy.

Referring to FIG. 9, FIG. 9 is a flow chart depicting a method 900 forperforming peer risk benchmarking, in accordance with an exampleembodiment.

Customer data for a network to be benchmarked, including roles ofnetwork devices and risk reports, is obtained at operation 910. Thecustomer data may include a description of roles of network devices andthe topology of the network, as well as risk reports for each dimensionof risk that is being assessed. For example, risk reports may includebest practices, security advisories, field notices, bug reports, servicerequests, system logs, end-of-life reports, licensing reports, and thelike. Additionally, risk reports can be divided into further dimensionsbased on the severity of reports, such as “low” or “high”, for each riskreport dimension.

A network profile image is generated based on the customer data atoperation 920. Initially, a risk score may be determined for eachdimension of risk based on the corresponding risk reports, descriptionsof roles of network devices, and/or topology of the network. Theresulting individual risk scores can together be converted into anetwork profile image that depicts risk across all assessed dimensions.For example, the risk scores may be used to construct a radar chart,such as radar chart 400 shown in FIG. 4, from which a polygon-shapednetwork profile image can be extracted. Transform operations and/orupscaling operations may be applied to finalize the network profileimage, and may exaggerate features that represent risk in order to moreeasily identify differences between compared images.

The network profile image is compared to one or more synthetic networkprofile images to determine differences at operation 930. The shapeand/or features of the network profile image may be compared to theshape and/or features of each synthetic network profile images, or boththe network profile image and the one or more synthetic network profileimages may be converted back to risk scores for each dimension of risk,which may then be compared.

An evaluation of the network is presented at operation 940. Theevaluation may indicate how the evaluated network compares to peers interms of risk for each dimension of risk that is assessed. For example,the evaluation may indicate to an organization that its network isfalling behind its peers' networks in terms of a particular dimension ofrisk, such as end-of-life. Thus, the organization may update anycommonly-used software and/or hardware indicated in the end-of-lifereports in order to raise the organization's risk score in thatdimension.

Referring to FIG. 10, FIG. 10 illustrates a hardware block diagram of acomputing device 1000 that may perform functions associated withoperations discussed herein in connection with the techniques depictedin FIGS. 1-9. In various embodiments, a computing device, such ascomputing device 1000 or any combination of computing devices 1000, maybe configured as any entity/entities as discussed for the techniquesdepicted in connection with FIGS. 1-9 in order to perform operations ofthe various techniques discussed herein.

In at least one embodiment, the computing device 1000 may include one ormore processor(s) 1002, one or more memory element(s) 1004, storage1006, a bus 1008, one or more network processor unit(s) 1010interconnected with one or more network input/output (I/O) interface(s)1012, one or more I/O interface(s) 1014, and control logic 1020. Invarious embodiments, instructions associated with logic for computingdevice 1000 can overlap in any manner and are not limited to thespecific allocation of instructions and/or operations described herein.

In at least one embodiment, processor(s) 1002 is/are at least onehardware processor configured to execute various tasks, operationsand/or functions for computing device 1000 as described herein accordingto software and/or instructions configured for computing device 1000.Processor(s) 1002 (e.g., a hardware processor) can execute any type ofinstructions associated with data to achieve the operations detailedherein. In one example, processor(s) 1002 can transform an element or anarticle (e.g., data, information) from one state or thing to anotherstate or thing. Any of potential processing elements, microprocessors,digital signal processor, baseband signal processor, modem, PHY,controllers, systems, managers, logic, and/or machines described hereincan be construed as being encompassed within the broad term ‘processor’.

In at least one embodiment, memory element(s) 1004 and/or storage 1006is/are configured to store data, information, software, and/orinstructions associated with computing device 1000, and/or logicconfigured for memory element(s) 1004 and/or storage 1006. For example,any logic described herein (e.g., control logic 1020) can, in variousembodiments, be stored for computing device 1000 using any combinationof memory element(s) 1004 and/or storage 1006. Note that in someembodiments, storage 1006 can be consolidated with memory element(s)1004 (or vice versa), or can overlap/exist in any other suitable manner.

In at least one embodiment, bus 1008 can be configured as an interfacethat enables one or more elements of computing device 1000 tocommunicate in order to exchange information and/or data. Bus 1008 canbe implemented with any architecture designed for passing control, dataand/or information between processors, memory elements/storage,peripheral devices, and/or any other hardware and/or software componentsthat may be configured for computing device 1000. In at least oneembodiment, bus 1008 may be implemented as a fast kernel-hostedinterconnect, potentially using shared memory between processes (e.g.,logic), which can enable efficient communication paths between theprocesses.

In various embodiments, network processor unit(s) 1010 may enablecommunication between computing device 1000 and other systems, entities,etc., via network I/O interface(s) 1012 to facilitate operationsdiscussed for various embodiments described herein. In variousembodiments, network processor unit(s) 1010 can be configured as acombination of hardware and/or software, such as one or more Ethernetdriver(s) and/or controller(s) or interface cards, Fibre Channel (e.g.,optical) driver(s) and/or controller(s), and/or other similar networkinterface driver(s) and/or controller(s) now known or hereafterdeveloped to enable communications between computing device 1000 andother systems, entities, etc. to facilitate operations for variousembodiments described herein. In various embodiments, network I/Ointerface(s) 1012 can be configured as one or more Ethernet port(s),Fibre Channel ports, and/or any other I/O port(s) now known or hereafterdeveloped. Thus, the network processor unit(s) 1010 and/or network I/Ointerface(s) 1012 may include suitable interfaces for receiving,transmitting, and/or otherwise communicating data and/or information ina network environment.

I/O interface(s) 1014 allow for input and output of data and/orinformation with other entities that may be connected to computer device1000. For example, I/O interface(s) 1014 may provide a connection toexternal devices such as a keyboard, keypad, a touch screen, and/or anyother suitable input and/or output device now known or hereafterdeveloped. In some instances, external devices can also include portablecomputer readable (non-transitory) storage media such as databasesystems, thumb drives, portable optical or magnetic disks, and memorycards. In still some instances, external devices can be a mechanism todisplay data to a user, such as, for example, a computer monitor, adisplay screen, or the like.

In various embodiments, control logic 1020 can include instructionsthat, when executed, cause processor(s) 1002 to perform operations,which can include, but not be limited to, providing overall controloperations of computing device; interacting with other entities,systems, etc. described herein; maintaining and/or interacting withstored data, information, parameters, etc. (e.g., memory element(s),storage, data structures, databases, tables, etc.); combinationsthereof; and/or the like to facilitate various operations forembodiments described herein.

The programs described herein (e.g., control logic 1020) may beidentified based upon application(s) for which they are implemented in aspecific embodiment. However, it should be appreciated that anyparticular program nomenclature herein is used merely for convenience;thus, embodiments herein should not be limited to use(s) solelydescribed in any specific application(s) identified and/or implied bysuch nomenclature.

In various embodiments, entities as described herein may storedata/information in any suitable volatile and/or non-volatile memoryitem (e.g., magnetic hard disk drive, solid state hard drive,semiconductor storage device, random access memory (RAM), read onlymemory (ROM), erasable programmable read only memory (EPROM),application specific integrated circuit (ASIC), etc.), software, logic(fixed logic, hardware logic, programmable logic, analog logic, digitallogic), hardware, and/or in any other suitable component, device,element, and/or object as may be appropriate. Any of the memory itemsdiscussed herein should be construed as being encompassed within thebroad term ‘memory element’. Data/information being tracked and/or sentto one or more entities as discussed herein could be provided in anydatabase, table, register, list, cache, storage, and/or storagestructure: all of which can be referenced at any suitable timeframe. Anysuch storage options may also be included within the broad term ‘memoryelement’ as used herein.

Note that in certain example implementations, operations as set forthherein may be implemented by logic encoded in one or more tangible mediathat is capable of storing instructions and/or digital information andmay be inclusive of non-transitory tangible media and/or non-transitorycomputer readable storage media (e.g., embedded logic provided in: anASIC, digital signal processing (DSP) instructions, software[potentially inclusive of object code and source code], etc.) forexecution by one or more processor(s), and/or other similar machine,etc. Generally, memory element(s) 1004 and/or storage 1006 can storedata, software, code, instructions (e.g., processor instructions),logic, parameters, combinations thereof, and/or the like used foroperations described herein. This includes memory element(s) 1004 and/orstorage 1006 being able to store data, software, code, instructions(e.g., processor instructions), logic, parameters, combinations thereof,or the like that are executed to carry out operations in accordance withteachings of the present disclosure.

In some instances, software of the present embodiments may be availablevia a non-transitory computer useable medium (e.g., magnetic or opticalmediums, magneto-optic mediums, CD-ROM, DVD, memory devices, etc.) of astationary or portable program product apparatus, downloadable file(s),file wrapper(s), object(s), package(s), container(s), and/or the like.In some instances, non-transitory computer readable storage media mayalso be removable. For example, a removable hard drive may be used formemory/storage in some implementations. Other examples may includeoptical and magnetic disks, thumb drives, and smart cards that can beinserted and/or otherwise connected to a computing device for transferonto another computer readable storage medium. A computer readablestorage medium, as used herein, is not to be construed as beingtransitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Variations and Implementations

Embodiments described herein may include one or more networks, which canrepresent a series of points and/or network elements of interconnectedcommunication paths for receiving and/or transmitting messages (e.g.,packets of information) that propagate through the one or more networks.These network elements offer communicative interfaces that facilitatecommunications between the network elements. A network can include anynumber of hardware and/or software elements coupled to (and incommunication with) each other through a communication medium. Suchnetworks can include, but are not limited to, any local area network(LAN), virtual LAN (VLAN), wide area network (WAN) (e.g., the Internet),software defined WAN (SD-WAN), wireless local area (WLA) access network,wireless wide area (WWA) access network, metropolitan area network(MAN), Intranet, Extranet, virtual private network (VPN), Low PowerNetwork (LPN), Low Power Wide Area Network (LPWAN), Machine to Machine(M2M) network, Internet of Things (IoT) network, Ethernetnetwork/switching system, any other appropriate architecture and/orsystem that facilitates communications in a network environment, and/orany suitable combination thereof.

Networks through which communications propagate can use any suitabletechnologies for communications including wireless communications (e.g.,4G/5G/nG, IEEE 802.11 (e.g., Wi-Fi®/Wi-Fi6®), IEEE 802.16 (e.g.,Worldwide Interoperability for Microwave Access (WiMAX)),Radio-Frequency Identification (RFID), Near Field Communication (NFC),Bluetooth™, mm.wave, Ultra-Wideband (UWB), etc.), and/or wiredcommunications (e.g., T1 lines, T3 lines, digital subscriber lines(DSL), Ethernet, Fibre Channel, etc.). Generally, any suitable means ofcommunications may be used such as electric, sound, light, infrared,and/or radio to facilitate communications through one or more networksin accordance with embodiments herein. Communications, interactions,operations, etc. as discussed for various embodiments described hereinmay be performed among entities that may directly or indirectlyconnected utilizing any algorithms, communication protocols, interfaces,etc. (proprietary and/or non-proprietary) that allow for the exchange ofdata and/or information.

In various example implementations, entities for various embodimentsdescribed herein can encompass computing elements (which can includevirtualized network elements, functions, etc.) such as, for example,laptop computers, tablet computers, netbook computers, personalcomputers (PCs), desktop computers, personal digital assistants (PDAs),smart phones, thin clients, network appliances, forwarders, routers,servers, switches, gateways, bridges, load balancers, firewalls,processors, modules, radio receivers/transmitters, or any other suitabledevice, component, element, or object operable to exchange informationor execute computer readable program instructions as described forvarious embodiments herein. Note that with the examples provided herein,interaction may be described in terms of one, two, three, or fourentities. However, this has been done for purposes of clarity,simplicity and example only. The examples provided should not limit thescope or inhibit the broad teachings of systems, networks, etc.described herein as potentially applied to a myriad of otherarchitectures.

Communications in a network environment can be referred to herein as‘messages’, ‘messaging’, ‘signaling’, ‘data’, ‘content’, ‘objects’,‘requests’, ‘queries’, ‘responses’, ‘replies’, etc. which may beinclusive of packets. As referred to herein and in the claims, the term‘packet’ may be used in a generic sense to include packets, frames,segments, datagrams, and/or any other generic units that may be used totransmit communications in a network environment. Generally, a packet isa formatted unit of data that can contain control or routing information(e.g., source and destination address, source and destination port,etc.) and data, which is also sometimes referred to as a ‘payload’,‘data payload’, and variations thereof. In some embodiments, control orrouting information, management information, or the like can be includedin packet fields, such as within header(s) and/or trailer(s) of packets.Internet Protocol (IP) addresses discussed herein and in the claims caninclude any IP version 4 (IPv4) and/or IP version 6 (IPv6) addresses.

To the extent that embodiments presented herein relate to the storage ofdata, the embodiments may employ any number of any conventional or otherdatabases, data stores or storage structures (e.g., files, databases,data structures, data or other repositories, etc.) to store information.

Note that in this Specification, references to various features (e.g.,elements, structures, nodes, modules, components, engines, logic, steps,operations, functions, characteristics, etc.) included in ‘oneembodiment’, ‘example embodiment’, ‘an embodiment’, ‘anotherembodiment’, ‘certain embodiments’, ‘some embodiments’, ‘variousembodiments’, ‘other embodiments’, ‘alternative embodiment’, and thelike are intended to mean that any such features are included in one ormore embodiments of the present disclosure, but may or may notnecessarily be combined in the same embodiments. Note also that amodule, engine, client, controller, function, logic or the like as usedherein in this Specification, can be inclusive of an executable filecomprising instructions that can be understood and processed on aserver, computer, processor, machine, compute node, combinationsthereof, or the like and may further include library modules loadedduring execution, object files, system files, hardware logic, softwarelogic, or any other executable modules.

It is also noted that the operations and steps described with referenceto the preceding figures illustrate only some of the possible scenariosthat may be executed by one or more entities discussed herein. Some ofthese operations may be deleted or removed where appropriate, or thesesteps may be modified or changed considerably without departing from thescope of the presented concepts. In addition, the timing and sequence ofthese operations may be altered considerably and still achieve theresults taught in this disclosure. The preceding operational flows havebeen offered for purposes of example and discussion. Substantialflexibility is provided by the embodiments in that any suitablearrangements, chronologies, configurations, and timing mechanisms may beprovided without departing from the teachings of the discussed concepts.

As used herein, unless expressly stated to the contrary, use of thephrase ‘at least one of’, ‘one or more of’, ‘and/or’, variationsthereof, or the like are open-ended expressions that are bothconjunctive and disjunctive in operation for any and all possiblecombination of the associated listed items. For example, each of theexpressions ‘at least one of X, Y and Z’, ‘at least one of X, Y or Z’,‘one or more of X, Y and Z’, ‘one or more of X, Y or Z’ and ‘X, Y and/orZ’ can mean any of the following: 1) X, but not Y and not Z; 2) Y, butnot X and not Z; 3) Z, but not X and not Y; 4) X and Y, but not Z; 5) Xand Z, but not Y; 6) Y and Z, but not X; or 7) X, Y, and Z.

Additionally, unless expressly stated to the contrary, the terms‘first’, ‘second’, ‘third’, etc., are intended to distinguish theparticular nouns they modify (e.g., element, condition, node, module,activity, operation, etc.). Unless expressly stated to the contrary, theuse of these terms is not intended to indicate any type of order, rank,importance, temporal sequence, or hierarchy of the modified noun. Forexample, ‘first X’ and ‘second X’ are intended to designate two ‘X’elements that are not necessarily limited by any order, rank,importance, temporal sequence, or hierarchy of the two elements. Furtheras referred to herein, ‘at least one of’ and ‘one or more of’ can berepresented using the ‘(s)’ nomenclature (e.g., one or more element(s)).

One or more advantages described herein are not meant to suggest thatany one of the embodiments described herein necessarily provides all ofthe described advantages or that all the embodiments of the presentdisclosure necessarily provide any one of the described advantages.Numerous other changes, substitutions, variations, alterations, and/ormodifications may be ascertained to one skilled in the art and it isintended that the present disclosure encompass all such changes,substitutions, variations, alterations, and/or modifications as fallingwithin the scope of the appended claims.

The programs described herein are identified based upon the applicationfor which they are implemented in a specific embodiment. However, itshould be appreciated that any particular program nomenclature herein isused merely for convenience, and thus the described embodiments shouldnot be limited to use solely in any specific application identifiedand/or implied by such nomenclature.

In one form, a method is provided comprising: obtaining customer datafor a first network, wherein the customer data comprises a role of oneor more network devices in the first network and a plurality of riskreports corresponding to the one or more network devices, and whereineach risk report is associated with a particular dimension of aplurality of dimensions of risk for the one or more network devices,generating a network profile image by processing the plurality of riskreports, using a generative adversarial network, generating a syntheticnetwork profile image from the network profile image, wherein thesynthetic network profile image does not include the customer data, andevaluating a second network using the synthetic network profile image toidentify differences between the first network and the second network.

In another form, the method further includes evaluating of the secondnetwork by identifying differences between the second network and one ormore additional networks using one or more synthetic profile imagescorresponding to the one or more additional networks.

In another form, the network profile image is generated based on aplurality of risk scores, wherein each risk score corresponds to adimension of risk of the plurality of dimensions of risk. In anotherform, the network profile image is descriptive of a polygon havingvertices defined by the plurality of risk scores represented in amultidimensional model that corresponds to the plurality of dimensionsof risk.

In another form, evaluating the second network using the syntheticnetwork profile image includes generating a second network profile imagefor the second network by processing customer data of the secondnetwork, and comparing the second network profile image to the syntheticnetwork profile image to identify differences, in the plurality ofdimensions of risk, between the first network and the second network.

In another form, the generative adversarial network upsamples thenetwork profile image, and wherein generating the synthetic networkprofile image is based on the upsampled network profile image.

In another form, the plurality of dimensions of risk include one or moreof: a best practices dimension, a security advisories dimension, a fieldnotices dimension, an end-of-life dimension, a network topologydimension, a network device roles dimension, a service requestdimension, a bug report dimension, a service request dimension, and asystem log dimension.

In one form, a computer system is provided, comprising: one or morecomputer processors; one or more computer readable storage media;program instructions stored on the one or more computer readable storagemedia for execution by at least one of the one or more computerprocessors, the program instructions comprising instructions to: obtaincustomer data for a first network, wherein the customer data comprises arole of one or more network devices in the first network and a pluralityof risk reports corresponding to the one or more network devices, andwherein each risk report is associated with a particular dimension of aplurality of dimensions of risk for the one or more network devices,generate a network profile image by processing the plurality of riskreports, use a generative adversarial network, generating a syntheticnetwork profile image from the network profile image, wherein thesynthetic network profile image does not include the customer data, andevaluate a second network using the synthetic network profile image toidentify differences between the first network and the second network.

In one form, one or more computer readable storage media is provided,the one or more computer readable storage media collectively havingprogram instructions embodied therewith, the program instructionsexecutable by a computer to cause the computer to: obtain customer datafor a first network, wherein the customer data comprises a role of oneor more network devices in the first network and a plurality of riskreports corresponding to the one or more network devices, and whereineach risk report is associated with a particular dimension of aplurality of dimensions of risk for the one or more network devices,generate a network profile image by processing the plurality of riskreports, use a generative adversarial network, generating a syntheticnetwork profile image from the network profile image, wherein thesynthetic network profile image does not include the customer data, andevaluate a second network using the synthetic network profile image toidentify differences between the first network and the second network

The descriptions of the various embodiments have been presented forpurposes of illustration, but are not intended to be exhaustive orlimited to the embodiments disclosed. Many modifications and variationswill be apparent to those of ordinary skill in the art without departingfrom the scope and spirit of the described embodiments. The terminologyused herein was chosen to best explain the principles of theembodiments, the practical application or technical improvement overtechnologies found in the marketplace, or to enable others of ordinaryskill in the art to understand the embodiments disclosed herein.

1. A computer-implemented method comprising: obtaining customer data fora first network, wherein the customer data comprises a role of one ormore network devices in the first network and a plurality of riskreports corresponding to the one or more network devices, and whereineach risk report is associated with a particular dimension of aplurality of dimensions of risk for the one or more network devices;generating a network profile image by processing the plurality of riskreports, wherein the network profile image includes a plurality of riskscores that are based on a count of risk reports for each dimension ofthe plurality of dimensions of risk; using a generative adversarialnetwork, generating a synthetic network profile image from the networkprofile image, wherein the synthetic network profile image does notinclude the customer data; and evaluating a second network by comparinga second network profile image, corresponding to the second network, tothe synthetic network profile image generated from the network profileimage of the first network to identify differences between the firstnetwork and the second network.
 2. The computer-implemented method ofclaim 1, wherein the evaluating of the second network comprisesidentifying differences between the second network and one or moreadditional networks by comparing the second network profile image to oneor more additional synthetic profile images corresponding to the one ormore additional networks.
 3. (canceled)
 4. The computer-implementedmethod of claim 1, wherein the network profile image is descriptive of apolygon having vertices defined by the plurality of risk scoresrepresented in a multidimensional model that corresponds to theplurality of dimensions of risk.
 5. The computer-implemented method ofclaim 1, wherein evaluating the second network using the syntheticnetwork profile image comprises: generating the second network profileimage for the second network by processing customer data of the secondnetwork; and comparing the second network profile image to the syntheticnetwork profile image to identify differences, in the plurality ofdimensions of risk, between the first network and the second network. 6.The computer-implemented method of claim 4, wherein the generativeadversarial network upsamples the network profile image to produce anupsampled network profile image, and wherein generating the syntheticnetwork profile image is based on the upsampled network profile image.7. The computer-implemented method of claim 1, wherein the plurality ofdimensions of risk include one or more of: a best practices dimension, asecurity advisories dimension, a field notices dimension, an end-of-lifedimension, a network topology dimension, a network device rolesdimension, a service request dimension, a bug report dimension, aservice request dimension, and a system log dimension.
 8. A computersystem comprising: one or more computer processors; one or more computerreadable storage media; and program instructions stored on the one ormore computer readable storage media for execution by at least one ofthe one or more computer processors, the program instructions comprisinginstructions to: obtain customer data for a first network, wherein thecustomer data comprises a role of one or more network devices in thefirst network and a plurality of risk reports corresponding to the oneor more network devices, and wherein each risk report is associated witha particular dimension of a plurality of dimensions of risk for the oneor more network devices; generate a network profile image by processingthe plurality of risk reports, wherein the network profile imageincludes a plurality of risk scores that are based on a count of riskreports for each dimension of the plurality of dimensions of risk; use agenerative adversarial network, generating a synthetic network profileimage from the network profile image, wherein the synthetic networkprofile image does not include the customer data; and evaluate a secondnetwork by comparing a second network profile image, corresponding tothe second network, to the synthetic network profile image to identifydifferences between the first network and the second network.
 9. Thecomputer system of claim 8, wherein the instructions to evaluate thesecond network comprise instructions to identify differences between thesecond network and one or more additional networks by comparing thesecond network profile image to one or more additional synthetic profileimages corresponding to the one or more additional networks. 10.(canceled)
 11. The computer system of claim 8, wherein the networkprofile image is descriptive of a polygon having vertices defined by theplurality of risk scores represented in a multidimensional model thatcorresponds to the plurality of dimensions of risk.
 12. The computersystem of claim 8, wherein the instructions to evaluate the secondnetwork using the synthetic network profile image comprise instructionsto: generate the second network profile image for the second network byprocessing customer data of the second network; and compare the secondnetwork profile image to the synthetic network profile image to identifydifferences, in the plurality of dimensions of risk, between the firstnetwork and the second network.
 13. The computer system of claim 11,wherein the generative adversarial network upsamples the network profileimage to produce an upsampled network profile image, and whereingenerating the synthetic network profile image is based on the upsamplednetwork profile image.
 14. The computer system of claim 8, wherein theplurality of dimensions of risk include one or more of: a best practicesdimension, a security advisories dimension, a field notices dimension,an end-of-life dimension, a network topology dimension, a network deviceroles dimension, a service request dimension, a bug report dimension, aservice request dimension, and a system log dimension.
 15. A computerprogram product comprising one or more computer readable storage mediacollectively having program instructions embodied therewith, the programinstructions executable by a computer to cause the computer to: obtaincustomer data for a first network, wherein the customer data comprises arole of one or more network devices in the first network and a pluralityof risk reports corresponding to the one or more network devices, andwherein each risk report is associated with a particular dimension of aplurality of dimensions of risk for the one or more network devices;generate a network profile image by processing the plurality of riskreports, wherein the network profile image includes a plurality of riskscores that are based on a count of risk reports for each dimension ofthe plurality of dimensions of risk; use a generative adversarialnetwork, generating a synthetic network profile image from the networkprofile image, wherein the synthetic network profile image does notinclude the customer data; and evaluate a second network by comparing asecond network profile image, corresponding to the second network, tothe synthetic network profile image generated from the network profileimage of the first network to identify differences between the firstnetwork and the second network.
 16. The computer program product ofclaim 15, wherein the instructions to evaluate the second network causethe computer to identify differences between the second network and oneor more additional networks by comparing the second network profileimage to one or more additional synthetic profile images correspondingto the one or more additional networks.
 17. (canceled)
 18. The computerprogram product of claim 15, wherein the network profile image isdescriptive of a polygon having vertices defined by the plurality ofrisk scores represented in a multidimensional model that corresponds tothe plurality of dimensions of risk.
 19. The computer program product ofclaim 15, wherein the instructions to evaluate the second network usingthe synthetic network profile image cause the computer to: generate thesecond network profile image for the second network by processingcustomer data of the second network; and compare the second networkprofile image to the synthetic network profile image to identifydifferences, in the plurality of dimensions of risk, between the firstnetwork and the second network.
 20. The computer program product ofclaim 15, wherein the plurality of dimensions of risk include one ormore of: a best practices dimension, a security advisories dimension, afield notices dimension, an end-of-life dimension, a network topologydimension, a network device roles dimension, a service requestdimension, a bug report dimension, a service request dimension, and asystem log dimension.
 21. The computer-implemented method of claim 1,wherein the risk scores for each dimension of risk are normalized to asame range of values.
 22. The computer system of claim 8, wherein therisk scores for each dimension of risk are normalized to a same range ofvalues.
 23. The computer program product of claim 15, wherein the riskscores for each dimension of risk are normalized to a same range ofvalues.